pipefy-observability
Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history.
- Installs
- 688
- Last 24h
- +11
- Trending rank
- #8359
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/pipefy/ai-toolkit --skill pipefy-observability- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pipefy-observability automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Observability
Read the MCP reference or CLI reference for the surface you are using. Load only the relevant reference.
Monitor AI agent and automation execution, usage stats, credit consumption, and export job history.
Identifiers reference
Full identifier map: observability identifiers.
| Concept | What tools expect | How to obtain |
|---|---|---|
| Pipe for AI agent logs | repo_uuid — the pipe UUID | get_pipe with numeric pipe_id; use pipe.uuid. |
| Automation for logs | automation_id — numeric | get_automations pipe_id=... |
| Org for usage stats | organization_uuid — UUID or numeric org id | get_organization returns the uuid; a numeric id also works (resolved server-side). Execution-metrics / export take numeric organization_id. |
Tools
| Operation | Read-only | Purpose |
|---|---|---|
get_ai_agent_logs | Yes | Execution history for a specific AI agent. |
get_ai_agent_log_details | Yes | Single execution detail for an AI agent log entry. |
get_automation_logs | Yes | Execution history for an automation (by automation ID). |
get_automation_logs_by_repo | Yes | Automation logs filtered by pipe. |
get_agents_usage | Yes | Org-level AI agent execution count and trends. |
get_automations_usage | Yes | Org-level automation execution stats. |
get_automation_execution_metrics | Yes | Per-automation execution metrics over FIFTEEN_MINUTES, SIXTY_MINUTES, TWELVE_HOURS, or TWENTY_FOUR_HOURS; partial success returns partial_errors for denied ids. |
get_ai_credit_usage | Yes | AI credit consumption and remaining balance. |
export_automation_jobs | No | Trigger async export of automation job history. |
get_automation_jobs_export | Yes | Poll export job status (after export_automation_jobs). |
get_automation_jobs_export_csv | Yes | Download finished automation-jobs export as CSV text. |
Steps — diagnose a failing AI agent
-
Get the pipe UUID (not the numeric pipe ID):
Operation:
get_pipe pipe_id=67890Capture
pipe.uuidfrom the response. -
Fetch recent agent logs:
Operation:
get_ai_agent_logs repo_uuid=<UUID> first=30 -
Identify the failed execution — look for
status: failedentries. -
Check credit usage if the agent stopped unexpectedly:
Operation:
get_ai_credit_usage organization_uuid=123 period="current_month" -
Fix and re-enable — update the agent config (see
pipefy-ai-agents) and toggle status:Operation:
toggle_ai_agent_statuswith the agent UUID andactive=true(SDK:agent_uuid; MCP:uuid).
Steps — export automation history as CSV
-
Trigger the export:
Operation:
export_automation_jobs organization_id=123 period="current_month" -
Poll for completion:
Operation:
get_automation_jobs_export export_id=<EXPORT_ID>Repeat until
statusisfinishedorfailed.A finished export also carries
fileUrl, a signed link to the xlsx. It expires 1 hour after the read that returned it, and anyone holding it can download the file until then. When you hand it to the user, say that it expires in 1 hour; for a fresh link, poll again. -
Fetch CSV text (when finished):
Operation:
get_automation_jobs_export_csv export_id=<EXPORT_ID>
Success criteria
- Agent logs show execution timestamps and statuses.
- Credit usage shows remaining balance; no unexpected drops.
- CSV export downloads successfully and contains expected automation history.
Failure modes
get_ai_agent_logsreturns empty: use the pipe UUID (e.g.,abc123-...), not the numeric pipe ID. Get UUID fromget_pipe.get_automation_jobs_exportstays inprocessing: large exports take time. Wait at least 60 seconds between polls. If stillprocessingafter several minutes, retry the export trigger.- Credit usage shows 0 remaining: alert the user — AI features will stop working until credits are replenished. Escalate to the Pipefy admin.
See also
pipefy-ai-agents— create and configure AI agents.pipefy-automations— create and debug automation rules.
FAQ
What does the pipefy-observability skill do?
Use this skill when the user wants to check AI agent logs, automation execution logs, org-level usage stats, AI credit consumption, or export automation job history.
How do I install pipefy-observability?
Run npx skills add https://github.com/pipefy/ai-toolkit --skill pipefy-observability in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pipefy-observability/ (personal) or .claude/skills/pipefy-observability/ (project).
Which AI agents can use pipefy-observability?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pipefy-observability safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains pipefy-observability?
It is published by pipefy in pipefy/ai-toolkit. It has 688 recorded installs.
More from pipefy/ai-toolkit
pipefy-automations
pipefy/ai-toolkit
Use this skill when the user wants to create, read, update, or delete traditional automations (if/then rules) or AI automations (prompt-driven). For AI agents (conversational), see pipefy-ai-agents.
pipefy-pipes-and-cards
pipefy/ai-toolkit
Use this skill when the user wants to read, create, update, or delete pipes, phases, phase fields, labels, cards, comments, or field conditions. Use the seed-pipe-across-phases workflow when populating empty phases for demos or QA.
pipefy-api-fallback
pipefy/ai-toolkit
Use this skill when a toolkit operation fails AND the introspection skill could not resolve the problem. This is the last-resort fallback (Tier 3): call the Pipefy GraphQL API directly using curl or httpx, authenticating with the Service Account (OAuth2) or a Personal Access Token (PAT) available as env var. Follow the 3-tier resolution strategy before reaching this point.
pipefy-introspection
pipefy/ai-toolkit
Use this skill when you need to discover GraphQL type shapes, mutation signatures, enum values, or execute arbitrary GraphQL as a fallback. This is the first fallback tier (Tier 2) when dedicated operations fail or don't exist for an operation.
pipefy-portal-setup
pipefy/ai-toolkit
Use this skill when the user wants to list, create, or configure Pipefy portals (main hub, pages, page elements, sub-portals, publish/unpublish). Uses Interfaces and internalapi. Not for pipes/cards.
pipefy-process-design
pipefy/ai-toolkit
Use this skill ONLY when the user explicitly asks for help designing or architecting a process ("help me design", "what's the best structure", "how do I organize this flow?"). For direct execution requests ("create a pipe for X", "build a reimbursement process", or any message with a detailed spec), skip this skill and execute directly using the domain skills (pipes-and-cards, automations, etc.).
Popular devops & cloud skills
microsoft-foundry
microsoft/azure-skills
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure
azure-diagnostics
microsoft/azure-skills
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.
azure-prepare
microsoft/azure-skills
Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
azure-ai
microsoft/azure-skills
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
azure-deploy
microsoft/azure-skills
Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.
azure-validate
microsoft/azure-skills
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.